Data & AI Project Lead

Title: Data & AI Project Lead Company: Ipsen Pharma (SAS) About Ipsen: Ipsen is a mid-sized global biopharmaceutical company with a focus on transformative medicines in three therapeutic areas: Oncology, Rare Disease and Neuroscience. Supported by nearly 100 years of development experience, with global hubs in the U.S., France and the U.K, we tackle areas of high unmet medical need through research and innovation. Our passionate teams in more than 40 countries are focused on what matters and endeavor every day to bring medicines to patients in 88 countries. We build a workplace that champions human-centric leadership and fosters a culture of collaboration, excellence and impact. At Ipsen, every individual is empowered to be their true selves, grow and thrive alongside the company’s success. Join us on our journey towards sustainable growth, creating real impact on patients and society! For more information, visit us at https://www.ipsen.com/ and follow our latest news on LinkedIn and Instagram . Job Description: Job Title: Data & AI Project Lead Division / Function: GDIT – Global Data & AI Division Manager’s Job Title: Head of Data & AI Product Management and Delivery Ipsen Job Profile: Data Analytics Location: Paris / London WHAT - Summary & Purpose of the Position : The Global Digital IT (GDIT) division is dedicated to being the business partner of choice for digital, data, and technologies, delivering outstanding value at speed and scale for patients and Ipsen. In this transformative context, GDIT plays a crucial role in driving innovation and efficiency across the organization. Within GDIT, the Global Data & AI Division was established with the mission to act as a business enabler. This department is responsible for driving the strategic use of Data and AI across Ipsen and enhance business interoperability. The Data & Analytics Manager leads the end-to-end delivery of cross-functional Data, Analytics and AI projects. The role is not permanently assigned to a specific business domain and may be deployed across different initiatives based on business priorities, project complexity and portfolio needs. Working closely with business sponsors, Data & AI Product Delivery Leads, IT Business Partners, technical teams and external delivery partners, the Data & Analytics Manager translates project objectives into structured delivery plans and coordinates execution from initial scoping through implementation, adoption and operational handover. The role ensures that projects are delivered in line with their agreed scope, timeline, budget, quality, security and compliance expectations, while maintaining clear governance, proactive risk management and effective stakeholder communication. Key responsibilities include: • Lead the end-to-end delivery of Data, Analytics and AI projects across business domains. • Structure project scope, objectives, governance, delivery plans, resources and budgets. • Coordinate business, functional, data, technology and external delivery teams. • Monitor delivery progress, dependencies, risks, issues, decisions and project financials. • Ensure that business requirements are translated into feasible and valuable Data & AI solutions. • Communicate project status, outcomes and business impact to sponsors and stakeholders. • Support solution adoption and coordinate transition to operational teams. • Contribute to the continuous improvement of Data & AI project delivery standards and practices. WHAT - Main Responsibilities & Technical Competencies A. Project Scoping and Planning • Work with business sponsors, Data & AI Product Delivery Leads and IT Business Partners to clarify project objectives, expected outcomes, scope and success criteria. • Structure projects into clear phases, milestones, deliverables and decision points. • Define project governance, delivery approach, resource requirements, budget assumptions and implementation plans. • Coordinate the assessment of business requirements, data availability, functional feasibility, technical options, architecture implications and delivery scenarios. • Ensure that assumptions, constraints, dependencies and responsibilities are clearly documented and agreed by the relevant stakeholders. • Establish measurable project outcomes and value indicators in collaboration with business owners. B. End-to-End Project Delivery • Lead Data, Analytics and AI projects from initiation and discovery through design, build, deployment, adoption and operational handover. • Coordinate cross-functional delivery teams composed of business, data, analytics, AI, engineering, architecture, security, compliance and change management contributors. • Maintain an integrated project plan and ensure effective coordination across workstreams and delivery partners. • Track project execution against the agreed scope, timeline, budget, quality and expected outcomes. • Ensure timely escalation and resolution of delivery issues and cross-project dependencies. • Adapt the delivery approach to the nature and maturity of each initiative, including proof of concept, MVP, industrialization, migration or enhancement projects. • Ensure appropriate documentation and readiness for transition to operational support teams. C. Project Governance, Risks and Financial Management • Establish and operate fit-for-purpose project governance, including project team meetings, working groups, steering committees and decision forums. • Prepare clear project status reports covering progress, achievements, milestones, budget, risks, issues, dependencies and decisions required. • Maintain project risk, issue, action and decision logs and ensure that owners and resolution plans are clearly identified. • Proactively identify delivery risks and implement mitigation or recovery plans. • Monitor project expenditure and resource consumption against the approved budget and forecast. • Support project financial planning, purchase order follow-up and vendor consumption monitoring, in coordination with the relevant finance and procurement contacts. • Escalate material deviations and provide sponsors with clear options and recommendations. D. Business Requirements, Data and Solution Coordination • Facilitate collaboration between business stakeholders and Data & AI experts to translate business challenges into clear requirements and actionable project deliverables. • Coordinate the definition and validation of business requirements, data requirements, use cases, user journeys and acceptance criteria. • Ensure that analytical outputs, reports and Data & AI solutions are understandable, actionable and aligned with business needs. • Coordinate data profiling and feasibility activities with the relevant data owners and technical teams. • Ensure th